Bilgisayar Mühendisliği

CE 477 | Ders Tanıtım Bilgileri

Dersin Adı

Data Science

Kodu	Yarıyıl	Teori (saat/hafta)	Uygulama/Lab (saat/hafta)	Yerel Kredi	AKTS
CE 477	FALL	3	0	3	5

Ön-Koşul(lar)	Array
Dersin Dili	English
Dersin Türü	ELECTIVE_COURSE
Dersin Düzeyi	Lisans
Dersin Veriliş Şekli	Face-To-Face
Dersin Öğretim Yöntem ve Teknikleri	Group Work Problem Solving Lecture / Presentation
Ulusal Meslek Sınıflandırma Kodu	-
Dersin Koordinatörü	Dr. Öğr. Üyesi Alper Demir
Öğretim Eleman(lar)ı	Dr. Öğr. Üyesi Alper Demir
Yardımcı(ları)	-

Dersin Amacı

The course introduces the principles and methods of data science – learning from data for prediction and insight. The course covers the key data science topics including getting data, visualizing and exploring data, statistical analysis of data, and the data science’s use of machine learning. The course focuses on developing hands-on data skills by offering the students to complete a data science project.

Öğrenme Çıktıları

Bu dersi başarıyla tamamlayabilen öğrenciler;

Ad	Açıklama	PC Alt	* Katkı Düzeyi
Ad	Açıklama	PC Alt	1	2	3	4	5
LO1	define computer tools to obtain, clean, and analyze data,	1.5				X
LO2	apply statistical methods and visualization to explore data,	1.3				X
LO3	use statistical and computational methods to make predictions from data,	4				X
LO4	perform data analysis with machine learning methods,	2				X
LO5	use statistics and data visualization tools to communicate the outcome of data analyses.	4				X

Ders Tanımı

The following topics will be included: getting and cleaning data, exploring data, statistical models of data, statistical inference, main machine learning methods in data science including linear regression, SVM, k-nearest neighbors, Naïve Bayes, logistic regression, decision trees, random forests, clustering, and dimensionality reduction, over-fitting, cross-validation, feature engineering.

Dersin İlişkili Olduğu Sürdürülebilir Kalkınma Amaçları

Dersin Kategorisi	Temel Ders
	Uzmanlık/Alan Dersleri	X
	Destek Dersleri
	İletişim ve Yönetim Becerileri Dersleri
	Aktarılabilir Beceri Dersleri

HAFTALIK KONULAR VE İLGİLİ ÖN HAZIRLIK ÇALIŞMALARI

Hafta	Konular	Ön Hazırlık	Öğrenme Çıktısı
1	Introduction	Chapter 1	LO1
2	Input: Concepts, instances, attributes	Chapter 2	LO1
3	Output: Knowledge representation	Chapter 3	LO1
4	Data Visualization and Preprocessing	Chapter 7	LO2
5	Classification and Regression	Chapter 4	LO4
6	Time Series Analysis	Chapter 4	LO3
7	Association Mining	Chapter 4	LO4
8	Midterm Exam		-
9	Clustering	Chapter 4	LO4
10	Evaluation	Chapter 5	LO3
11	Ensemble Learning	Chapter 6	LO4
12	Data Science Extensions and Applications	Chapter 8	LO5
13	Data Science Extensions and Applications	Chapter 8	LO5
14	Data Science Extensions and Applications	Chapter 8	LO5
15	Review of the Semester		-
16	Final Exam		-

Ders Kitabı

I. E. Witten et al “Data Mining: Practical Machine Learning Tools and Techniques” Morgan Kaufmann 2016 ISBN 978-0128042915

Önerilen Okumalar/Materyaller

J. Grus “Data Science from Scratch: First Principles with Python” O’Reilly Media 2015 ISBN 9781491901427- 9781491904381 (Ebook)
T. Hastie R. Tibshirani J. Friedman “The Elements of Statistical Learning” Springer 2013 ISBN 9780387216065
S. Raschka “Python Machine Learning” Packt Publishing 2015 ISBN 9781783555147
R. D. Peng E. Matsui “The Art of Data Science” https://leanpub.com/artofdatascience
Han Jiawei Jian Pei and Hanghang Tong. Data mining: concepts and techniques. Morgan kaufmann 2022.

DEĞERLENDİRME ÖLÇÜTLERİ

Yarıyıl Aktiviteleri	Sayı	Katkı Payı %	LO1	LO2	LO3	LO4	LO5
Proje	1	30	X	X	X	X	X
Ara Sınav	1	30	X	X	X	X	X
Final Sınavı	1	40	X	X	X	X	X
Toplam	3	100

AKTS / İŞ YÜKÜ TABLOSU

Yarıyıl Aktiviteleri	Sayı	Süre (Saat)	İş Yükü
Katılım	-	-	-
Teorik Ders Saati	16	3	48
Laboratuvar / Uygulama Ders Saati	-	-	-
Sınıf Dışı Ders Çalışması	14	2	28
Arazi Çalışması	-	-	-
Küçük Sınav / Stüdyo Kritiği	-	-	-
Portfolyo	-	-	-
Ödev	-	-	-
Sunum / Jüri Önünde Sunum	-	-	-
Proje	1	24	24
Seminer/Çalıştay	-	-	-
Sözlü Sınav	-	-	-
Ara Sınavlar	1	25	25
Final Sınavı	1	25	25
		Toplam	150

DERSİN ÖĞRENME ÇIKTILARININ PROGRAM YETERLİLİKLERİ İLE İLİŞKİSİ

#	PC Alt	Program Yeterlilikleri / Çıktıları	* Katkı Düzeyi
#	PC Alt	Program Yeterlilikleri / Çıktıları	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering				LO2
	4	Computation
	5	Related engineering discipline-specific topics				LO1
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.					LO4
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.					LO3 LO5
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.